The majority of businesses today operate on legacy software systems. Specifically, more than 70% of the software used by Fortune 500 companies is at least 20 years old.

This software may still be functional, but what about the price in terms of investment vs. the received value? Aging systems accumulate growing software maintenance cost factors due to outdated architecture, undocumented code, and dependencies on the leading experts who understand the code completely. With each year, your software becomes less efficient in meeting business demands, while you need to invest more money and resources just to keep it afloat.

U.S. companies spend up to 80% of their IT budgets on maintaining existing technologies, leaving just 20% for innovation. This disparity exacerbates every year and restrains business growth opportunities.

Legacy applications increase costs and degrade business performance:

• Extended testing cycles because of complex dependencies 
• More complicated debugging due to poor documentation 
• Inability to implement modern tools
• Developer team burnout due to endless fixes

We’ll explain why maintaining modern software is much more beneficial than outdated systems, which factors magnify expenses, and how to optimize annual software maintenance, enabling innovative development.

Key Factors Influencing Software Maintenance Costs

Let’s start by identifying which factors expand your maintenance expenses, year after year.

Code complexity and lack of modularity

Non-modular systems accumulate dependencies that trigger cascading issues with every change. Developers must navigate tangled code to implement even minor updates. This increases risks, time, and cost.

Outdated technology stacks and unsupported libraries

Systems created a long time ago often rely on obsolete frameworks and unsupported libraries. Without vendor updates or compatible tools, teams must rewrite code and patch integrations manually. Compounding technical debts increase security and performance issues, while continual fault fixing consumes unjustified resources.

Dependency on hard-coded integrations and legacy APIs

Old APIs and hard-coded integrations lock the system’s functionality with restricted external services or data formats. When external services change or age, even small updates require extensive rework, creating costly bottlenecks and blocking scalability.

Skill shortage: Fewer developers are familiar with old tech

As legacy technologies age, fewer developers remain proficient in them, which leads to limited availability. Eventually, scarce expertise drives up rates and slows knowledge transfer, turning routine maintenance into a time-consuming expense.

Lack of automated testing and CI/CD pipelines

The absence of automated testing increases manual effort required for quality assurance. The result? Larger operational costs, potential mistakes, and delayed deliveries. The releases are further extended by the absence of CI/CD pipelines, which turns revisions and debugging into a burden. Organizations spend budget and time fixing what could have been prevented rather than enhancing functionality.

Unclear ownership and documentation debt

With the lack of documentation and ownership information, developers can neither identify who made the latest code modifications nor understand the decisions of previous teams. The outcomes include inefficient onboarding and troubleshooting, plenty of redundant effort, and compounding long-term maintenance costs.

Andrew Lychuk highlights the business impact: “Technical debt without documentation undermines the whole process and the whole system. Every undocumented fix becomes tomorrow’s $10,000 problem.”

Maintenance Cost Differences: Legacy Monolithic vs. Modern Architectures

The cost of maintenance in software engineering differs sharply between legacy monoliths and modern modular systems, reflecting variations in technology, risk, and long-term sustainability.

Scope of change

In legacy monoliths, every change can affect the entire system. Developers must run full regression tests to make even minor updates, since tightly coupled code risks unpredictable side effects.

Modular or microservice architectures, however, allow updates within isolated units, without any risk to the entire system.

How does it impact performance and software maintenance cost?

Maintenance complexity

The following software quality metrics can help you measure maintainability:

• Cyclomatic complexity: calculates the number of possible code paths, which translates into potential ease of testing and debugging.
• Modularity index: reflects the quality of the system’s division into independent, reusable parts.
• Halstead volume: describes how much effort is required to understand, test, or modify code.

Infrastructure and operational expenses

Software maintenance costs heavily depend on the utilized infrastructure. Legacy systems rely on on-premises servers that require manual patching and constant upkeep, while modern architectures leverage automated, scalable cloud services.

The human factor

A major hidden risk in legacy systems is tribal knowledge, undocumented logic known only to a few developers.

Modern architectures reduce this risk through shared documentation, CI/CD pipelines, and standardized practices.

The Impact of Technical Debt and Poor Documentation on Maintenance Costs

Every shortcut in software development carries a hidden price tag that will show up in the future.

Igor Omelianchuk explains: “Very often, developers create non-optimized code with some hard codes and tricky stuff inside. If all this additional or non-optimized work is not documented, the new developer who will try to fix some bug won’t be able to understand what is going on in the system. If the system doesn’t have clues, fixing an issue may take forever.”

These compromises compound technical debt that would later result in extra maintenance or refactoring effort. A small compromise today evolves into a financial burden tomorrow, leading to the escalating software maintenance cost percentage.

The hidden cost of missing documentation

Without clear documentation, new developers waste hours and even days exploring legacy functions and trying to understand the intent behind old code.

Igor Omelianchuk illustrates: “When documentation is absent, a new developer will have to go through cycles of trying faults when new bugs are introduced to the system, struggling to fix and understand the current approaches in the code.”

Apart from delaying deliveries, this also raises average software maintenance cost through repeated rework and investigation that would be needless with documentation in place.

The “Bus Factor” risk

The so-called “Bus Factor” is also the consequence of poor documentation and entails the accumulation of critical knowledge with just several engineers. The rest of the team becomes, therefore, dependent on those people: if they leave, the operations may stall. Other developers will have to fight to restore lost knowledge, which inflicts higher onboarding costs, impeded bug resolution, and a greater risk of downtime.

Andrew Lychuk warns: “If one person leaving can stop your business, that’s not loyalty, that’s risk.”

The cumulative effect of documentation debt

Documentation debt creates a snowball effect over time. Every missing comment or undocumented API elevates IT maintenance costs and slows onboarding. Relying on a few key engineers may seem safe, but it creates dependency and potential misuse of insider knowledge. Clean documentation protects both technical and business integrity.

Technical and documentation debts are more than merely technical concerns. They are significant business risks.

Igor Omelianchuk summarizes: “You need to have good documentation, ideally good structured code with explaining all the tricky turnarounds and features.”

Essential Metrics and KPIs for Measuring Maintenance Efficiency

When seeking how to reduce software maintenance cost, you should identify ways to elevate the maintenance efficiency.  Tracking key efficiency metrics reveals weaknesses, optimizes resource allocation, and justifies modernization investments.

Mean Time to Repair (MTTR)

MTTR measures how much time it takes to identify a failed asset and restore the system to the initial state. High MTTR is the sign of slow recovery, unclear ownership, or excessive complexity. This is typical for legacy environments with poor monitoring and documentation.

Mean Time Between Failures (MTBF)

MTFB shows the average operational time between system failures. Low MTBF is inherent in systems lacking stability and prone to frequent breakdowns, caused by outdated infrastructure, technical debt, or lack of automated testing.

Change Failure Rate (CFR)

CFR demonstrates the percentage of software changes that lead to downtime, rollbacks, or other issues. High values can be caused by fragile code and limited test coverage. Each update can inflict downtime or rollback, common in legacy systems.

Cost per Change Request (CPCR)

The average cost of implementing a particular change or feature. Every change in monolithic code may require full regression testing, involving extensive resources and escalating costs.

Test Coverage Ratio

The percentage of code that can be tested automatically. Low coverage is usually the case with legacy systems, posing the risk of undetected regressions and leading to unpredictable maintenance.

Code Duplication Ratio

The percentage of repeated or redundant code blocks in the codebase. With duplicated code, any emerging bug may appear in numerous places, extending the fixing and maintenance effort.

Comment Density

This indicator demonstrates the ratio of comments to lines of code, showing whether the code is properly documented. Little comments mean a lack of documentation and hidden logic, while excessive comments may witness unclear or complex code.

Coupling & Cohesion

Coupling assesses the interdependence between modules. Cohesion measures the relation between the functions within a module. High coupling and low cohesion mean a fragile code and tight interdependencies. Low coupling improves scalability and speeds up maintenance.

Technical Debt Ratio

The estimated cost of fixing maintainability issues (bugs, refactoring needs, missing tests) compared to the cost of developing new functionality. A high ratio indicates excessive resources spent on maintenance, with lower capacity to innovate.

Maintenance-to-Development Budget Ratio

The part of IT maintenance expenses for keeping old systems alive versus implementing innovations. A ratio above 80% indicates an unjustified legacy load.

Outdated systems often conceal performance issues until they turn into costly failures. Tracking these KPIs helps address gaps through refactoring and modernization, redirecting budgets from  reactive fixes to optimized maintenance.

Balancing Maintenance Costs with Innovation and Feature Development

We often refer to the balancing of enterprise software maintenance costs between maintenance and innovation, which may lead to a misconception when the two are viewed as competitors for the same budget. Igor Omelianchuk specifies: “Maintenance and innovation are not the opposing priorities; they’re different stages of your business evolution.”

Maintenance protects and stabilizes existing assets, and modernization prepares the business for what comes next.

Continual maintenance guarantees that current systems retain security, compliance, and focus on your business needs. Maintenance is necessary to prevent costly breakdowns and keep trust with clients and partners. Essentially, it creates a solid ground for further innovation efforts.

Modernization, in turn, comes as a natural next step. Innovation isn’t an auxiliary option, rather a necessity. Even properly maintained applications will once reach the end of their lifecycles, typically in 15-17 years. Then, significant modernization is required to restore performance and security.

The right approach: Strategic investment cycles

System maintenance and innovations should be balanced on a distance, not year to year. The correct strategy includes allocating separate budgets for modernization cycles every 5–10 years. It allows businesses to spread costs over time, ensure continuous improvement, and prevent financial emergencies caused by unexpected technical faults and rebuilds.

That said, modernization must start with careful analysis that addresses technical debt and hidden causes of stagnation. Andrew Lychuk warns: “Never rewrite yesterday’s mistakes in tomorrow’s code.” Otherwise, you will reproduce old inefficiencies in a new form. Competent modernization combines refactoring, reengineering, and rebuilding to make your systems scalable, flexible, and cost-efficient.

Conclusion

Maintenance can keep business systems afloat for a long time. But without modernization, it only delays the end of the software lifecycle. Every year, the maintenance cost of software expands with the accumulated tech debt, old team habits, and documentation gaps. These factors accelerate the rise of maintenance expenses until a full rebuild becomes unavoidable.

Organizations should recognize that maintenance alone is necessary, but not sufficient. Preventive support must be coupled with planned modernization cycles that reinvent architectures, fight tech debt, train teams, and match the system capacity with business demands.

A proactive strategy integrates modernization into maintenance to control expenses, reduce risk, and enable innovation. The essential goal of maintenance isn’t to make the old system work well today, but to prepare it for tomorrow.